Intern, Applied AI

Rockwell Automation•Austin, TX

18h•$22 - $40•Hybrid

About The Position

The internship will focus on creating AI agents capable of analyzing industrial control requirements and Programmable Logic Controller (PLC) applications, automatically generating comprehensive test programs and validation plans, and executing these tests within simulation environments. This project emphasizes open, reproducible research through the use of publicly available PLC programs, open-source tools, synthetic simulation environments, and non-proprietary datasets. An important objective is enabling the AI system to move beyond standard functional testing and systematically identify corner cases, fault conditions, safety risks, and unexpected operating scenarios that may not be specified by developers. The project will leverage publicly available PLC programs, open-source control platforms, simulation environments, and synthetic datasets, allowing researchers to conduct the research using non-proprietary data and reproducible benchmarks.

Requirements

Must be pursuing a bachelor's or advanced degree from an accredited college or university

Nice To Haves

Prior intern or co-op with Rockwell Automation
Pursuing a degree in Mechanical Engineering, artificial intelligence, engineering, or similar
Cumulative GPA of 3.0 or above
Prior experience in delivering AI-enabled process improvements
Expected graduation date is December 2027 or beyond
Met on campus, national recruiting events, a Rockwell Automation-hosted event or a targeted recruiting campaign
Previous experience in a manufacturing environment
Experience in the following Python, machine learning or Generative AI, reinforcement learning, industrial automation, software testing, formal methods, robotics or simulation environments

Responsibilities

Develop agentic AI workflows that automatically create test plans from PLC code, functional specifications, and system requirements.
Create AI agents that create and refine test cases during PLC code development.
Identify and test corner cases, rare events, and abnormal operating conditions.
Produce fault-injection scenarios including sensor failures, actuator faults, communication disruptions, and unexpected process disturbances.
Evaluate safety-related behaviors and verify compliance with operational constraints.
Validate conformance between control goals and implemented PLC logic.
Integrate simulation-driven testing and automated result analysis into the validation workflow.
Explore formal verification and reasoning techniques to improve confidence in generated test plans.
Emphasize open, reproducible research through the use of publicly available PLC programs, open-source tools, synthetic simulation environments, and non-proprietary datasets.